AI and Machine Learning
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AI and Machine Learning Syllabus — Unit-wise Content
The official GTU curriculum for AI and Machine Learning (subject code 3170924) as prescribed for Electrical Engineering · Semester 7 divides the course into 6 units, covering 29 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3170924)Unit 1 Introduction
- Scope of the Course
- Introduction to AI, Brief review of History of AI, Related fields
Unit 2 Introduction to Artificial Neural Networks
- Biological Neurons and Biological Neural Networks
- Artificial Neural Networks
- Activation Functions
- Perceptron NN, Multilayer Perceptron NN, Back-propagation Neural Networks
- Training Methods
- Basic definition of supervised and unsupervised Learning
Unit 3 Introduction to Machine Learning
- Introduction (Different Types of Learning) Hypothesis Space
- Inductive Bias
- Evaluation and Cross Validation
Unit 4 Main Algorithms used in Machine Learning
- Linear Regression
- Decision Trees
- Learning Decision Trees
- K-nearest Neighbour
- Collaborative Filtering
- Dimensionality Reduction Technique :Feature Selection
- Feature Extraction
Unit 5 Logistic Regression and Support Vector Machine
- Logistic Regression
- Introduction to Support Vector Machine
- The Dual Formation
- Maximum Margin with Noise
- Nonlinear SVM and Kernel Function
- SVM: Solution to the Dual Problem
Unit 6 Advanced Learning methods and Clustering
- Introduction to Clustering
- K- means Clustering
- Agglomerative Hierarchical Clustering
- Basics of Semi- Supervised and Reinforcement Learning
- Introduction to Deep Learning 06 Subject Code: 3170924 w.e.f
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